apache-airflow-providers-papermill

v3.13.0 safe
3.0
Low Risk

Provider package apache-airflow-providers-papermill for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package does not exhibit any high-risk behaviors such as making network calls, harvesting credentials, or using obfuscation techniques that could indicate malicious intent. The incomplete author information and new/inactive account raise minor concerns but do not strongly suggest a supply-chain attack.

  • No network calls detected.
  • No evidence of credential harvesting.
  • Incomplete author information and potentially new account.
Per-check LLM notes
  • Network: No network calls detected, which is normal for this type of package.
  • Shell: The shell execution pattern appears to be related to running Jupyter kernels, which is consistent with the package's purpose and not indicative of malicious activity.
  • Obfuscation: The observed pattern is likely a standard technique for extending module search paths and not indicative of malicious activity.
  • Credentials: No patterns indicative of credential harvesting or secret theft were detected.
  • Metadata: The author information is incomplete and the account seems new or inactive, which raises some concern. However, there are no clear signs of typosquatting or suspicious activity from the git repository or email domain.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 13 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
  • 13 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/apache-airflow-providers-pap
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4101 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 6 type-annotated function signatures (partial)
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 46 unique contributor(s) across 100 commits in apache/airflow
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # # Licensed to the Apache
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • e_kernel(request): proc = subprocess.Popen( [ "python3", "-m",
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: airflow.apache.org>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository apache/airflow appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with apache-airflow-providers-papermill 
Develop a data processing pipeline using Apache Airflow and the 'apache-airflow-providers-papermill' package. This pipeline will serve as a mini-app that automates the execution of Jupyter Notebooks as part of a workflow, allowing for dynamic data analysis and reporting. Here’s a detailed breakdown of the project requirements and steps to create it:

1. **Project Setup**
   - Set up a virtual environment for your project.
   - Install necessary packages including `apache-airflow`, `apache-airflow-providers-papermill`, and any dependencies required for your Jupyter Notebook tasks.

2. **Define Your Workflow**
   - Create a DAG (Directed Acyclic Graph) that represents your workflow. This DAG will include tasks that use Papermill to execute Jupyter Notebooks dynamically.
   - Each task in the DAG should have parameters that can be passed to the Jupyter Notebook, such as input data files or configuration settings.

3. **Create Jupyter Notebooks**
   - Develop one or more Jupyter Notebooks that perform specific data processing tasks, such as data cleaning, feature engineering, or model training.
   - Ensure these notebooks accept parameters via Papermill to allow for flexibility and reusability.

4. **Parameterization and Dynamic Execution**
   - Use the 'apache-airflow-providers-papermill' package to parameterize your Jupyter Notebooks from within Airflow tasks.
   - Demonstrate how different configurations or datasets can be passed to the same notebook to produce varying outputs.

5. **Integration and Testing**
   - Test your DAG locally to ensure all tasks run correctly and interact as expected.
   - Deploy your DAG to an Airflow instance and monitor its execution.

6. **Documentation and Reporting**
   - Document each step of your pipeline and the rationale behind your design choices.
   - Automate the generation of reports based on the outputs of your Jupyter Notebooks.

7. **Advanced Features (Optional)**
   - Implement error handling and retry mechanisms for your tasks.
   - Integrate with external services like databases or cloud storage for data retrieval and storage.
   - Schedule your DAGs to run at regular intervals or based on specific events.

By completing this project, you will gain hands-on experience with Apache Airflow, Jupyter Notebooks, and the powerful integration capabilities provided by 'apache-airflow-providers-papermill'. This mini-app will serve as a foundational example for building more complex data pipelines in the future.

💬 Discussion Feed

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